arXiv subjects

Peng-Bi Cui

Publications and source records attributed to Peng-Bi Cui.

12 recordsLinked to original sources

arXiv2026

Distinct routes to phase transitions in spatial activation systems

Threshold-driven activation governs a wide range of collective phenomena, yet the microscopic origins of its phase transitions in spatial systems remain unresolved. Here, we show that spatial activation systems undergo multiple distinct routes to phase transitions, controlled by a single parameter---the interaction range. We uncover a unified phase diagram featuring continuous, first-order, and mixed-order transitions, and demonstrate that the two abrupt transitions arise from fundamentally different mechanisms: nucleation-driven front propagation and critical branching. These routes exhibit distinct dynamical scaling, establishing a direct link between microscopic activation dynamics and macroscopic critical behavior. We further identify a metastable phase in which global activation cannot be achieved by random activation alone, but can be triggered by localized seeds. In this regime, the critical activation nucleus remains finite and independent of system size, implying that arbitrarily large systems can remain stable under random perturbations yet highly vulnerable to localized triggers. The onset of this phase is abrupt, revealing an extreme sensitivity of collective dynamics to small parameter changes. These results establish a mechanistic framework for phase transitions in spatial activation systems and reveal how microscopic perturbations can trigger macroscopic cascades.

physics.soc-ph↗

arXiv2026

Macro-level reinforcement tunes the transition order of reversible social contagion

Social contagion is often shaped by reinforcement: individuals become more likely to adopt a new behavior, opinion, or product as exposure accumulates or adoption becomes widely visible. Existing network models mainly capture this effect through local mechanisms, such as threshold responses or higher-order interactions. However, how macro-level reinforcement reshapes reversible spreading remains unclear. Here we study a SIS-like process in which pairwise transmission is reinforced by global prevalence. Combining quasistationary simulations and bifurcation analysis, we show that global feedback can produce first-order transition and hysteresis loop, with distinct activation and collapse thresholds. We further show how network localization promotes local ignition while weakening the global prevalence signal required for abrupt macroscopic activation, thereby raising the reinforcement threshold. Our results reveal how onset--retreat asymmetry emerges from global feedback coupled to network structure, providing a minimal mechanism for abrupt, history-dependent reversible social contagion.

physics.soc-ph↗

arXiv2025

Network localization governs social contagion dynamics with macro-level reinforcement

The spread of ideas, behaviors, and technologies generally depends on feedback mechanisms operating across multiple scales. Previous studies have extensively examined pairwise transmission and local reinforcement. However, the role of macro-level social influence -- where widespread adoption enhances further adoption -- remains understudied. Here, we focus on a contagion process that incorporates both pairwise interactions and macro-level reinforcement. We show that the contagion undergoes a shift from continuous to mixed-order transition as macro-level influence exceeds a reinforcement threshold. Simulations on various real-world networks indicate that network localization governs the contagion outcomes by determining the critical point and the reinforcement threshold. Building on this insight, we develop a structural metric linking network localization to contagion dynamics, revealing a key trade-off: networks that facilitate weak contagion tend to experience slower diffusion and lower adoption rates, while networks that suppress weak contagions enable faster and more widespread adoption. These findings challenge the conventional belief that stronger local connectivity uniformly promotes contagion.

physics.soc-ph↗

arXiv2023

Exploring the formation dynamics of affective polarization by considering a coupled feedback

Polarization issue is generally subject to ideological polarization and affective polarization. In particular, affective polarization usually accelerates the polarization process and transform social interactions into a zero-sum game. Yet, a wide array of existing literature have not provided valid ways to make distinction between them. Therefore, the mechanism contributing to the rise of affective polarization still remain unclear, as well as its unique emergent dynamics. To address this issue, this study introduces the coupled feedback between opinions and response susceptibility to a attraction-repulsion model which takes account into three parameters: interaction strength, response susceptibility and tolerance to others. The model features phase diagrams of global consensus, affective polarization, and ``harmony with diversity" states. The simulations on time-varying and static social networks show that intermediate parameter ranges yield a global convergence, as one integrated cluster collapsing and converging towards a uncertain moderate position after long-time persistence. Overall, the simulations reveal that the feedback essentially offers a counterforce to establish an inversion between global convergence and ``harmony with diversity". Remarkably, strengthening feedback may facilitate polarization by driving the system priorly self-organize into one integrated cluster which then gradually approaching polarization, especially for low tolerance and strong interactions, and the step-like dynamic behaviors of opinion entropy suggest the occurrence of dynamic equilibrium. For the first time, this study attempts to offer a useful approach to the micro foundations of affective polarization, and the results guide us how to avoid the dilemmas from this polarization.

physics.soc-ph↗

arXiv2023

Identifying the structure patterns to govern the performance of localization in regulating innovation diffusion

The macro social influence is recognized as a non-negligible ingredient in innovation propagation: more adopters in the network lead to a higher adoption tendency for the rest individuals. A recent study to incorporate such a crucial mechanism shows that sufficient intensity of macro-level social influence can cause a change from a continuous to discontinuous transition, further indicating the existence of a tricritical point. Although network localization strength determines the tricritical point, it remains unclear what network quantities govern the performance of localization in regulating innovation diffusion. To address this issue, we herein consider the model incorporating both the micro- and macro-levels social influence. We present a dynamic message-passing method to analytically treat both the outbreak threshold and recovered population, and validate the predictions through agent-based simulations. Extensive analysis on the classical synthetic networks shows that sparsely available connections, and relatively heterogeneous degree distribution, either assortative or extremely disassortative configurations are favorable for continuous transition. In such cases, the employed network can yield a strong localization effect so that the innovation is trapped in the configurations composed of the hubs with high non-backtracking centrality. We further explore the dependence of both tricritical point and localization strength on three structural quantities: network density, heterogeneity, and assortativity, which gives a clear physical picture of the joint effects of the three structure quantities on the localization strength. Finally, we conclude that the core-periphery structure, being sensitive to the change of the three structure quantities, essentially determines localization strength, and further regulates the phase transition.

physics.soc-ph↗

arXiv2023

Exploring the foundation of social diversity and coherence with a novel attraction-repulsion model framework

One widely-existed state --``harmony with diversity" in which individuals freely express various viewpoints to sustain integration of social diversity, but at the same time shared values ensure social coherence, can be considered as the foundation of social diversity and coherence, however, which has never attracted research attention. Its formation mechanism still remains unclear. To address this issue, this study proposes an attraction-repulsion model based on the general simple assumption that individuals tend to either reach an agreement with shared opinions or to amplify difference from others with distant opinions. It allows us to take account into the three core parameters: interaction strength, individuals' susceptibility and tolerance to others' opinions. We are concerned with the effect of not only time-varying topology but also fixed interactions imposed by static social network, where the tasks of heterogeneous individuals' attributes are also performed. Remarkably, the simple model rules successfully generate the three above phases except for fragmentation, along with three different transitions and the triple points. We find that sufficient susceptibility, intermediate interaction strength and high tolerance can benefit a balance between repulsive and attractive forces, and thus the emergence of "harmony with diversity". However, fixed interactions can introduce cluster-level self-reinforced mechanism which can unexpectedly promote polarization. Heterogeneous susceptibility or tolerance turns out to be an inhibiting factor, which should be avoided. A method to identify the phase boundaries through computing the maximum susceptibility of entropy and stand deviation of opinions, confirmed by numerical simulations, allows us to build phase diagrams and to locate where the triple points are.

physics.soc-ph↗

arXiv2022

Network localization strength regulates innovation diffusion with macro-level social influence

Innovation diffusion in the networked population is an essential process that drives the progress of human society. Despite the recent advances in network science, a fundamental understanding of network properties that regulate such processes is still lacking. Focusing on an innovation diffusion model with pairwise transmission and macro-level social influence, i.e., more adopters in the networked population lead to a higher adoption tendency among the remaining individuals, we observe discontinuous phase transitions when the influence is sufficiently strong. Through extensive analyses of a large corpus of empirical networks, we show that the tricritical point depends on the network localization strength, which our newly proposed metric can effectively quantify. The metric reveals the deep connection between the critical and tricritical points and further indicates a trade-off: networks that allow less attractive products to prevail tend to yield slower diffusion and lower market penetration and verse versa. Guided by this trade-off, we demonstrate how marketers can rewire the networks to modulate product diffusion according to their needs.

physics.soc-ph↗

arXiv2018

Close and ordinary social contacts: how important are they in promoting large-scale contagion?

An outstanding problem of interdisciplinary interest is to understand quantitatively the role of social contacts in contagion dynamics. In general, there are two types of contacts: close ones among friends, colleagues and family members, etc., and ordinary contacts from encounters with strangers. Typically, social reinforcement occurs for close contacts. Taking into account both types of contacts, we develop a contact-based model for social contagion. We find that, associated with the spreading dynamics, for random networks there is coexistence of continuous and discontinuous phase transitions, but for heterogeneous networks the transition is continuous. We also find that ordinary contacts play a crucial role in promoting large scale spreading, and the number of close contacts determines not only the nature of the phase transitions but also the value of the outbreak threshold in random networks. For heterogeneous networks from the real world, the abundance of close contacts affects the epidemic threshold, while its role in facilitating the spreading depends on the adoption threshold assigned to it. We uncover two striking phenomena. First, a strong interplay between ordinary and close contacts is necessary for generating prevalent spreading. In fact, only when there are propagation paths of reasonable length which involve both close and ordinary contacts are large scale outbreaks of social contagions possible. Second, abundant close contacts in heterogeneous networks promote both outbreak and spreading of the contagion through the transmission channels among the hubs, when both values of the threshold and transmission rate among ordinary contacts are small. We develop a theoretical framework to obtain an analytic understanding of the main findings on random networks, with support from extensive numerical computations.

physics.soc-ph↗

arXiv2018

Effect of network clustering on mutually cooperative coinfections

The spread of an infectious disease can be promoted by previous infections with other pathogens. This cooperative effect can give rise to violent outbreaks, reflecting the presence of an abrupt epidemic transition. As for other diffusive dynamics, the topology of the interaction pattern of the host population plays a crucial role. It was conjectured that a discontinuous transition arises when there are relatively few short loops and many long loops in the contact network. Here we focus on the role of local clustering in determining the nature of the transition. We consider two mutually cooperative pathogens diffusing in the same population: an individual already infected with one disease has an increased probability of getting infected by the other. We look at how a disease obeying the susceptible-infected-removed dynamics spreads on contact networks with tunable clustering. Using numerical simulations we show that for large cooperativity the epidemic transition is always abrupt, with the discontinuity decreasing as clustering is increased. For large clustering strong finite size effects are present and the discontinuous nature of the transition is manifest only in large networks. We also investigate the problem of influential spreaders for cooperative infections, revealing that both cooperativity and clustering strongly enhance the dependence of the spreading influence on the degree of the initial seed.

physics.soc-ph↗

arXiv2017

Small world yields optimal public goods in presence of both altruistic and selfish cooperators

Empirical studies have shown that individuals' behaviors are largely influenced by social conformity, including punishment. However, a coevolutionary theoretical framework that takes into account effects of conformity on individuals' punishment behaviors has not been put forward yet. Herein we propose a coevolutionary game model to extend the theory of cooperation with conformity in spatial public goods game by considering pool punishment, as well as two converse feedback modes of conformity that strongly affect cooperators' punishment behaviors. We focus on how different parameters and spatial structures govern evolutionary dynamics on three different kinds of networks by employing mean-field analysis based on replicator dynamics and Monte Carlo simulations. On regular lattices, defectors are overall extincted since cooperators, especially selfish cooperators, have great evolutionary advantages due to strong network reciprocity, and at the same time the number of altruistic cooperators decays. Conversely, abundant shortcuts in regular random networks lead to the prevalence of altruistic cooperators, but cooperators suffer from free-riding behaviors of defectors. Of particular interest, we find that small-world topology can simultaneously help cooperators successfully outperform defectors by means of strong network reciprocity, and enable rich contacting opportunities with defectors to facilitate the expansion of altruistic cooperators. Therefore, we clarify that small world is the optimal topology subject to the dominance of altruistic cooperators.

physics.soc-ph↗

arXiv2017

Epidemic spreading dynamics with drug-resistant and heterogeneous contacts

Drug resistance and strong contacts actually play crucial roles in epidemic spread in complex systems. Nevertheless, neither theoretical model or methodology is proposed to address this. We thus consider an edge-based epidemic spread model considering the two key ingredients, in which the contacts are grouped into two classes: strong contacts and normal ones. Next, we present a unified edge-based compartmental approach to the spread dynamics on Erd\"{o}s-R\'{e}nyi (ER) networks and validate its results by extensive numerical simulations. In case that epidemic is totally drug-resistant, we both numerically and theoretically show a slow outbreak (continuous transition) of epidemics when number of strong contacts is not enough for the emergence of null threshold. If the epidemic owns partial resistance, we would observe evident faster-growing outbreaks (discontinuous transitions) and larger final epidemic sizes for few strong contacts, instead of emergence of null threshold with increase of strong contacts. Inhibiting effect of infection threshold, positive roles of strong contacts and strength of strong contacts in promoting outbreaks are also approved. Throughout this paper, we could drive exact predictions through the analytical approach, showing good agreements with numerical simulations.

physics.soc-ph↗

arXiv2017

Mutually cooperative epidemics on power-law networks

The spread of an infectious disease can, in some cases, promote the propagation of other pathogens favouring violent outbreaks, which cause a discontinuous transition to an endemic state. The topology of the contact network plays a crucial role in these cooperative dynamics. We consider a susceptible--infected--removed (SIR) type model with two mutually cooperative pathogens: an individual already infected with one disease has an increased probability of getting infected by the other. We present an heterogeneous mean-field theoretical approach to the co--infection dynamics on generic uncorrelated power-law degree-distributed networks and validate its results by means of numerical simulations. We show that, when the second moment of the degree distribution is finite, the epidemic transition is continuous for low cooperativity, while it is discontinuous when cooperativity is sufficiently high. For scale-free networks, i.e. topologies with diverging second moment, the transition is instead always continuous. In this way we clarify the effect of heterogeneity and system size on the nature of the transition and we validate the physical interpretation about the origin of the discontinuity.

physics.soc-ph↗